| Abstract: |
This comprehensive review paper presents a meta-analysis of experimental and numerical studies on the strength and behaviour of reinforced concrete (RC) structural elements. The investigation encompasses an extensive survey of existing literature, focusing on various RC members including beams, columns, slabs, and shear walls under diverse loading conditions. Modern computational techniques, particularly finite element methods (FEM) and machine learning approaches, have significantly enhanced our understanding of RC element behavior. This paper critically examines past research methodologies, highlighting the evolution from purely experimental approaches to sophisticated numerical simulations. The integration of experimental validation with numerical predictions has proven essential for understanding complex failure mechanisms and stress-strain relationships in RC structures. Key findings indicate that factors such as concrete compressive strength, reinforcement ratio, shear span-to-depth ratio, and loading rate substantially influence structural performance. Advanced nonlinear analysis techniques have enabled researchers to predict ultimate capacity and deformation characteristics with improved accuracy. This review synthesizes current knowledge, identifies research gaps, and proposes future directions for enhanced understanding of RC structural behavior. The paper also emphasizes the importance of material characterization, boundary conditions, and modeling assumptions in achieving reliable predictions. |
